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Abstract
Driven by global competiti on and e. 01’ ’Ilg C L1SIQmer needs and expcemt ion ,
manufacrurlng systems today have witnessed (l ~ignlfiCllnl increase in dynnmi behavior and
unstubl stale (i.e .. an attempt to shin lbe ?R.x-e:>:ifrom one operating level to another),
The run] rity of P methodologie: :;:;ume;;I steady-state static process bclI.wk.r
(i.e .. operating with a CQn:st:1Il1mean and coostant variunc wilbout the Influence of the dynamic
behavior. Traditional P has been uccessfully d in steady-slate rnauufacturing processe .
bur !’Cc(:.I1t1ythese opproachc are being recval uated for u e in dynarnlc behavior environments.
Quality control a .rlvitie hould nOI disturb Itt> flow or lhe producnon proce ’ ami must
cope with i nature. Hence. the use of SP methodologies 10 add ss processes tll;ll
are ill dynamic behavior mode h - starred 10 emerge.
The dynamic behavior of a manufacturing proce may be represented a a ysteru with
Inpll1 variable s,OUIPOfvariable. and. a noise disLUrbanot. n i1’llpOllailf uutcome of tIle d namtc
behavior is the induced transltion period (i.e., a temporal lren~J th’ll is inhe TIt to the dynamic
behavior nml the ourocorrelniion (i.e .. lhe dmil is not independen: • which comprosuse
the validity ~,rImdihOlltll SPC for monitoring the proces . Because 0 poor understanding and
control of the dynamic behavior. hrlloe produce and pound I ses otten result. While much
research effort ha been dedicated to II advancement of monitoring and adjustment
rncthodologi S (II S\C~HJy- ’till¢. process, so liulc (lltenUOIl has been gillen 10 the dy’n:l.llli
manufacturing proee sses,
he goal of this researela is 10 pr nl tip proces monitoring and ;Ldjll’tIllCi1’t
locthooolo.gics for addre Sing dynami bduwi r problems 0 tlWI Y l<’:nl performance
improvement may be attained. TIl<: methodologies will provide .t scientific approach to acquire
criuenl Imm ledge of the dynamic bella’ iOT as well 11;:improved onrrol and quali1” leading
to the enhancement of economic position. The three rnqjor developm Ills in thi re earch are:
I, TI characterlzation of the dymlJl1k behavior of the JmwElf;Jc(udllg prove ~ with
rbe app .opriat moniioring proced ures,
TIl” d velopmenr of adaptive monitoring plUCC IIIres for the process [for example. using
Trend char (e_g., linear model] Hj’l(l lill;e series charts e.g., RJMA ”l()(lt:ls)] wit);
ac rnprsison betweenunivariate andmuhivarlntc corurol hans.